It’s easy to make things complex without meaning to. Especially in statistical analysis.
Sometimes that complexity is unavoidable. You have ethical and practical constraints on your study design and variable measurement. Or the data just don’t behave as you expected. Or the only research question of interest is one that demands many variables.
But sometimes it isn’t. Seemingly innocuous decisions lead to complicated analyses. These decisions occur early in the design, research questions, or variable choice.
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Just about everyone who does any data analysis has used a chi-square test. Probably because there are quite a few of them, and they’re all useful.
But it gets confusing because very often you’ll just hear them called “Chi-Square test” without their full, formal name. And without that context, it’s hard to tell exactly what hypothesis that test is testing. (more…)
One component often overlooked in the ‘Define & Design’ phase of a study, is writing the analysis plan. The statistical analysis plan integrates a lot of information about the study including the research question, study design, variables and data used, and the type of statistical analysis that will be conducted.
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The field of statistics has a terminology problem.
It affects students’ ability to learn statistics. It affects researchers’ ability to communicate with statisticians; with collaborators in different fields; and of course, with the general public.
It’s easy to think the real issue is that statistical concepts are difficult. That is true. It’s not the whole truth, though. (more…)
Before you can write a data analysis plan, you have to choose the best statistical test or model. You have to integrate a lot of information about your research question, your design, your variables, and the data itself.
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Statistical inference using hypothesis testing is ubiquitous in science. Several misconceptions and misinterpretations of p-values have arisen over the years, which can lead to challenges communicating the correct interpretation of results.
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